Nowadays, state-of-the-art MTL approaches mainly focus on adaptively learning weights of different tasks in a mixture-of-experts structure [1]}[2]}[3]}[4]}[5]}.
This makes task weights changing in different samples so that tasks can share useful but not conflict information adaptively.
Such MTL approaches achieve promising performances in various scenarios.
However, when we use state-of-the-art MTL approaches for modeling the default/non-default task and the rejection/approval task, we do not achieve satisfactory performances, and even achieve poor performances in default prediction on rejected samples.
This may be because we have no observed default/non-default labels for rejected samples during model training. The task weights, which decide how much information is shared between the two tasks, are not well optimized in the feature distribution of rejected samples.
Thus, exiting MTL approaches fail in modeling the biased credit scoring data, and we need a novel and specially-designed MTL approach.
